Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-6001-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-6001-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Nov 2025
Research article |
| 20 Nov 2025
| 20 Nov 2025
Seasonal evolution of snow density and its impact on thermal regime of sea ice during the MOSAiC expedition
Yubing Cheng
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
Finnish Meteorological Institute, Helsinki, Finland
Roberta Pirazzini
Finnish Meteorological Institute, Helsinki, Finland
Amy R. Macfarlane
The Arctic University of Norway UiT, Tromsø, Norway
Northumbria University, Newcastle Upon Tyne, UK
Timo Vihma
Finnish Meteorological Institute, Helsinki, Finland
Wolfgang Dorn
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
Ruzica Dadic
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Martin Schneebeli
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Stefanie Arndt
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Annette Rinke
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
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Daria Paul, Danu Caus, Paul Keil, Christopher Kadow, and Stefanie Arndt
The Cryosphere, 20, 5491–5508, https://doi.org/10.5194/tc-20-5491-2026, https://doi.org/10.5194/tc-20-5491-2026, 2026
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We studied how snow and its properties vary across Antarctic sea ice using detailed field measurements combined with machine learning methods. We found that differences between ice types are mainly due to how snow layers are arranged rather than their basic properties. Snow changes over short distances, meaning single measurements often miss this complexity. Capturing this variability is key to improving climate models and satellite estimates.
Mara Neudert, Daria Paul, Sacha van der Vleuten, Stefanie Arndt, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2026-5496, https://doi.org/10.5194/egusphere-2026-5496, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Icebergs can strongly change how snow and ice develop on Antarctic coastal sea ice, but their effects are poorly understood. We combined drone mapping with measurements of snow and ice thickness and ice cores to examine these changes. We found that thick snow on the windward side of icebergs promotes flooding and freezes into solid snow-ice, greatly increasing ice thickness while reducing ice growth from below. Snow-free areas in the lee, in contrast, had thicker ice grown from below.
Mel Fülster, Ruzica Dadic, Iris Thurnherr, and Heini Wernli
EGUsphere, https://doi.org/10.5194/egusphere-2026-4721, https://doi.org/10.5194/egusphere-2026-4721, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Winter-time fast ice extent and variability in McMurdo Sound is governed by strong wind events. We investigate the large scale atmospheric forcing leading to winter sea ice breakout events in McMurdo Sound from 2013–2024 using satellite-derived sea ice concentration data, weather station data and ERA-5 reanalysis data. Our results indicate that cyclones in the Ross Sea impact sea ice in McMurdo Sound indirectly through the barrier wind regime known as the Ross Ice Shelf air stream.
Lejiang Yu, Shiyuan Zhong, Timo Vihma, and Jinlun Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2026-4204, https://doi.org/10.5194/egusphere-2026-4204, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Sea ice in the Weddell Sea, east of the Antarctic Peninsula, is an important part of the global climate system. Summertime sea-ice concentration variability has increased in the northern Weddell Sea but decreased in the southern Weddell Sea. These opposite changes are partly linked to large-scale atmospheric patterns connected to tropical regions near Australia and the Indian Ocean.
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 20, 4585–4604, https://doi.org/10.5194/tc-20-4585-2026, https://doi.org/10.5194/tc-20-4585-2026, 2026
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Understanding of the local effects of sea-ice concentration variations on the Arctic atmosphere is a prerequisite for assessing the role of Arctic sea-ice decline in the climate system, including its influence on mid-latitudes. In our study, using data from atmospheric reanalysis, we present how the relationships of sea-ice concentration, temperature, and specific humidity and their direction change depending on region and season over the Arctic.
Elena Shevnina, Timo Vihma, Miguel Potes, and Tuomas Naakka
Hydrol. Earth Syst. Sci., 30, 4721–4739, https://doi.org/10.5194/hess-30-4721-2026, https://doi.org/10.5194/hess-30-4721-2026, 2026
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The study first estimated the summertime evaporation over lakes located in coastal Antarctica with direct (eddy-covariance) measurements collected during two austral summers (December–January) in 2017–2018 and 2019–2020. The lake evaporation was on average 1.6 mm d-1 in the ice break-up period, and it doubled in the ice free period. The bulk aerodynamic method with a site-specific transfer coefficient of moisture well reproduced the observed day-to-day variations in evaporation over lakes.
Mengmeng Li, Jianwei Ma, Yang Li, Juha Karvonen, Bin Cheng, Yingfei Wang, Haili Li, Yafei Nie, and Zheng Duan
EGUsphere, https://doi.org/10.5194/egusphere-2026-2504, https://doi.org/10.5194/egusphere-2026-2504, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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We retrieved Arctic snow depth (2012–2021) using satellite data and four machine learning methods. A simple linear regression performed best against independent aircraft data, while complex models overfit. A measurement at 89 GHz was key for older ice. Using our snow depth data greatly reduced errors in ice thickness estimates. This provides a reliable Arctic snow depth dataset.
Ziyu Yan, Yufang Ye, Georg Heygster, Bin Cheng, Carolina Gabarró, Ferran Hernández-Macià, and Xiao Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2026-1680, https://doi.org/10.5194/egusphere-2026-1680, 2026
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Passive microwave retrieval of snow depth remains constrained by snow and ice conditions. By combining the Snow Microwave Radiative Transfer model with global sensitivity analysis, this study quantifies how snow grain size, density, and sea ice type affect snow depth retrievability across channels. The small grain size of new snow limits retrieval performance and increases its sensitivity to sea ice type, while the use of the 89 GHz channel may help address this limitation.
Xiyue Zhang, Minghu Ding, Diyi Yang, Bin Cheng, Ian Allison, Kongju Zhu, Yuande Yang, Xiangbin Cui, Petra Heil, Chuanjin Li, and Cunde Xiao
EGUsphere, https://doi.org/10.5194/egusphere-2026-2150, https://doi.org/10.5194/egusphere-2026-2150, 2026
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Stake measurements underestimate Antarctic surface mass balance (SMB) due to firn compaction. Using a firn densification model, we correct 2008-2024 stake records from Dome A. The corrected mean annual SMB is 24.14 kg m-2 yr-1, 8.8 % higher than uncorrected values, whereas RACMO2.4p1 gives 17.50 kg m-2 yr-1. The net vapor flux is equivalent to 5.7 % of the total mass input. This framework provides observational benchmarks for climate models, and supports oldest‑ice drilling at Dome A.
Cecilia Äijälä, Lucía Gutiérrez-Loza, Wolfgang Dorn, Siv K. Lauvset, Wieslaw Maslowski, and Petteri Uotila
EGUsphere, https://doi.org/10.5194/egusphere-2026-1849, https://doi.org/10.5194/egusphere-2026-1849, 2026
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This study compares coupled ocean–atmosphere models, reanalyses, and satellite data for 1991–2020 in the Barents and Kara Seas, where Arctic sea‑ice loss has been greatest. The results evaluate how well these products asses sea ice and ocean temperature. Reanalyses and satellites show strong sea‑ice decline, while models show little or none – only HIRHAM‑NAOSIM shows a weak decline in the Barents Sea. Reanalyses also indicate stronger mixed‑layer warming then the models, especially in autumn.
Chen Jiang, Lejiang Yu, Timo Vihma, and Jiechen Zhao
Atmos. Chem. Phys., 26, 4275–4288, https://doi.org/10.5194/acp-26-4275-2026, https://doi.org/10.5194/acp-26-4275-2026, 2026
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This study analyzes the spatial and temporal trends of Antarctic fast ice extent from 2000 to 2018. We also investigate the physical processes through which mid- and low-latitude sea surface temperature anomalies affect fast ice via atmospheric planetary waves, along with seasonal variations in climate forcing mechanisms.
Chao-Yuan Yang, Fengguan Gu, Jiping Liu, Annette Rinke, Hu Yang, and Xiaoxu Shi
EGUsphere, https://doi.org/10.5194/egusphere-2026-362, https://doi.org/10.5194/egusphere-2026-362, 2026
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Accurately exchanging energy between the atmosphere and the ocean-ice systems through surface heat fluxes is crucial for climate modelling. We present an improved version of Coupled Arctic Prediction System (CAPS) by revising flux coupling for conservation of heat. Our results show that the model with improved flux coupling can better simulate sea ice conditions during the period of Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC).
Jan Landwehrs, Sonja Murto, Florian Gebhardt, Ella Gilbert, and Annette Rinke
Weather Clim. Dynam., 7, 341–365, https://doi.org/10.5194/wcd-7-341-2026, https://doi.org/10.5194/wcd-7-341-2026, 2026
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This study investigates how Svalbard's mountains modulate warm and moist air mass intrusions into the central Arctic, where such events are key drivers of warm extremes. Using atmospheric modeling, air parcel trajectories and observations from the MOSAiC expedition for a case in April 2020 and a climatological analysis for springtime in 2000–2022, we show that Svalbard can alter winds, temperatures, clouds and surface energy fluxes hundreds of kilometers downstream over sea ice.
Jack C. Landy, Claude de Rijke-Thomas, Carmen Nab, Isobel Lawrence, Isolde A. Glissenaar, Robbie D. C. Mallett, Renée M. Fredensborg Hansen, Alek Petty, Michel Tsamados, Amy R. Macfarlane, and Anne Braakmann-Folgmann
The Cryosphere, 20, 183–208, https://doi.org/10.5194/tc-20-183-2026, https://doi.org/10.5194/tc-20-183-2026, 2026
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In this study, we use three satellites to test the planned remote sensing approach of the upcoming mission Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) over sea ice and that its dual radars will accurately measure the heights of the top and base of snow sitting atop floating sea ice floes. Our results suggest that CRISTAL's dual radars will not necessarily measure the snow top and base under all conditions. We find that accurate height measurements depend more on surface roughness than on snow properties, as is commonly assumed.
Amanda Sellmaier, Ellen Damm, Torsten Sachs, Benjamin Kirbus, Inge Wiekenkamp, Annette Rinke, Falk Pätzold, Daiki Nomura, Astrid Lampert, and Markus Rex
Atmos. Chem. Phys., 25, 17685–17700, https://doi.org/10.5194/acp-25-17685-2025, https://doi.org/10.5194/acp-25-17685-2025, 2025
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This study presents continuous ship-borne measurements of methane (CH4) concentration and isotopic composition monitored during an ice drift expedition in 2020. Using trajectory analysis, we linked atmospheric CH4 variabilities to air mass pathways transported over open water or sea-ice. The study highlights the potential of ship-borne observations to fill significant data gaps in the high Arctic.
Steven Franke, Mara Neudert, Veit Helm, Arttu Jutila, Océane Hames, Niklas Neckel, Stefanie Arndt, and Christian Haas
The Cryosphere, 19, 6319–6339, https://doi.org/10.5194/tc-19-6319-2025, https://doi.org/10.5194/tc-19-6319-2025, 2025
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Our research explored how icebergs affect the distribution of snow and flooding on Antarctic coastal sea ice. Using aircraft-based radar and laser scanning, we found that icebergs create thick snow drifts on their wind-facing sides and leave snow-free zones in their lee. The weight of these snow drifts often causes the ice below to flood, forming slush. These patterns, driven by wind and iceberg placement, are crucial for understanding sea ice changes and improving climate models.
Julia Martin, Ruzica Dadic, Brian Anderson, Roberta Pirazzini, Oliver Wigmore, and Lauren Vargo
The Cryosphere, 19, 6103–6126, https://doi.org/10.5194/tc-19-6103-2025, https://doi.org/10.5194/tc-19-6103-2025, 2025
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This study examines how snow distribution affects Antarctic sea ice surface temperature, a key factor in its energy balance. Using drone and ground-based data, we mapped snow depth and surface temperature on 2.4 m thick sea ice in McMurdo Sound. We corrected thermal camera inconsistencies and found that surface temperature is more influenced by topography-driven solar radiation than snow depth. Our findings highlight the need to account for small-scale processes in sea ice energy balance models.
Valerie Reppert, Olaf Eisen, Holger Schmithüsen, Stefanie Arndt, Guido Ascenso, Linda Ort, and Zsófia Jurányi
EGUsphere, https://doi.org/10.5194/egusphere-2025-5199, https://doi.org/10.5194/egusphere-2025-5199, 2025
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We analyzed 33 years of snow accumulation measurements at Neumayer Station, Antarctica, to understand changes in the amount of snow building up on the surface over time. The long-term, high-resolution data show mostly stable accumulation, but rare positive extremes appeared in 2021 and 2023. Seasonal and multi-year patterns were identified, providing valuable data to improve climate models and satellite observations.
Kristiina Verro, Cecilia Äijälä, Roberta Pirazzini, Ruzica Dadic, Damien Maure, Willem Jan van de Berg, Giacomo Traversa, Christiaan T. van Dalum, Petteri Uotila, Xavier Fettweis, Biagio Di Mauro, and Milla Johansson
The Cryosphere, 19, 4409–4436, https://doi.org/10.5194/tc-19-4409-2025, https://doi.org/10.5194/tc-19-4409-2025, 2025
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Accurately representing Antarctic sea ice is essential for reliable climate and ocean model predictions. We evaluated how different models simulate the sea ice's sunlight reflectivity (called albedo) using field and satellite data. Models with simple albedo schemes performed well in limited cases but missed key processes. The advanced scheme in the MetROMS-UHel ocean model provided the most accurate results, including observed day–night albedo changes observed during a field campaign.
Fangru Mu, Chengfei Jiang, Bin Cheng, Keguang Wang, Caixin Wang, Yuhan Chen, Zhiyuan Shao, and Jiechen Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2025-3214, https://doi.org/10.5194/egusphere-2025-3214, 2025
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Icebergs pose risks to ships and are an important part of the polar environment. We developed an iceberg detection algorithm based on the Swin transformer model (IDAS-Transformer). The IDAS-Transformer is capable of handling complex surface characteristic mixtures, including fast ice, pack ice, and open water, to identify icebergs.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David Anthony Bailey, and Petteri Uotila
Geosci. Model Dev., 18, 4823–4853, https://doi.org/10.5194/gmd-18-4823-2025, https://doi.org/10.5194/gmd-18-4823-2025, 2025
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The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 19, 1031–1046, https://doi.org/10.5194/tc-19-1031-2025, https://doi.org/10.5194/tc-19-1031-2025, 2025
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To better understand the local, regional, and global impacts of the recent rapid sea-ice decline in the Arctic, one of the key issues is to quantify the effects of sea-ice concentration on the surface radiative fluxes. We analyse these effects utilising four data sets called atmospheric reanalyses, and we evaluate uncertainties in these effects arising from inter-reanalysis differences in the sensitivity of the surface radiative fluxes to sea-ice concentration.
Puzhen Huo, Peng Lu, Bin Cheng, Miao Yu, Qingkai Wang, Xuewei Li, and Zhijun Li
The Cryosphere, 19, 849–868, https://doi.org/10.5194/tc-19-849-2025, https://doi.org/10.5194/tc-19-849-2025, 2025
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We developed a new method for retrieving lake ice phenology for a lake with complex surface cover. The method is particularly useful for mixed-pixel satellite data. We implement this method on Lake Ulansu, a lake characterized by complex shorelines and aquatic plants in northwestern China. In connection with a random forest model, we reconstructed the longest lake ice phenology in China.
Rui Xu, Chaofang Zhao, Stefanie Arndt, and Christian Haas
The Cryosphere, 18, 5769–5788, https://doi.org/10.5194/tc-18-5769-2024, https://doi.org/10.5194/tc-18-5769-2024, 2024
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The onset of snowmelt on Antarctic sea ice is an important indicator of sea ice change. In this study, we used two radar scatterometers to detect the onset of snowmelt on perennial Antarctic sea ice. Results show that since 2007, snowmelt onset has demonstrated strong interannual and regional variabilities. We also found that the difference in snowmelt onsets between the two scatterometers is closely related to snow metamorphism.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
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Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Lu Zhou, Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Shiming Xu, Weixin Zhu, Sahra Kacimi, Stefanie Arndt, and Zifan Yang
The Cryosphere, 18, 4399–4434, https://doi.org/10.5194/tc-18-4399-2024, https://doi.org/10.5194/tc-18-4399-2024, 2024
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Snow over Antarctic sea ice, influenced by highly variable meteorological conditions and heavy snowfall, has a complex stratigraphy and profound impact on the microwave signature. We employ advanced radiation transfer models to analyse the effects of complex snow properties on brightness temperatures over the sea ice in the Southern Ocean. Great potential lies in the understanding of snow processes and the application to satellite retrievals.
Lara Foth, Wolfgang Dorn, Annette Rinke, Evelyn Jäkel, and Hannah Niehaus
The Cryosphere, 18, 4053–4064, https://doi.org/10.5194/tc-18-4053-2024, https://doi.org/10.5194/tc-18-4053-2024, 2024
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It is demonstrated that the explicit consideration of the cloud dependence of the snow surface albedo in a climate model results in a more realistic simulation of the surface albedo during the snowmelt period in late May and June. Although this improvement appears to be relatively insubstantial, it has significant impact on the simulated sea-ice volume and extent in the model due to an amplification of the snow/sea-ice albedo feedback, one of the main contributors to Arctic amplification.
Falco Monsees, Alexei Rozanov, John P. Burrows, Mark Weber, Annette Rinke, Ralf Jaiser, and Peter von der Gathen
Atmos. Chem. Phys., 24, 9085–9099, https://doi.org/10.5194/acp-24-9085-2024, https://doi.org/10.5194/acp-24-9085-2024, 2024
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Cyclones strongly influence weather predictability but still cannot be fully characterised in the Arctic because of the sparse coverage of meteorological measurements. A potential approach to compensate for this is the use of satellite measurements of ozone, because cyclones impact the tropopause and therefore also ozone. In this study we used this connection to investigate the correlation between ozone and the tropopause in the Arctic and to identify cyclones with satellite ozone observations.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
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The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
Di Chen, Qizhen Sun, and Timo Vihma
EGUsphere, https://doi.org/10.5194/egusphere-2024-2359, https://doi.org/10.5194/egusphere-2024-2359, 2024
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We investigates the variations and trends in Arctic sea ice during summer and autumn, focusing on the impacts of sea surface temperature (SST) and surface air temperature (SAT). Both SST and SAT significantly influence Arctic sea ice concentration. SST affects both interannual variations and decadal trends, while SAT primarily influences interannual variations. Additionally, SAT's impact on sea ice concentration leads by seven months, due to a stronger warming trend in winter than in summer.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
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The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
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A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Zen Mariani, Sara M. Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie M. Hartten, Jareth Holt, Christopher J. Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data, 16, 3083–3124, https://doi.org/10.5194/essd-16-3083-2024, https://doi.org/10.5194/essd-16-3083-2024, 2024
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During the Year of Polar Prediction (YOPP), we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of datasets that have been collected to facilitate user uptake and model–observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODFs), including a description of the sites, data format, and instruments.
Stefanie Arndt, Nina Maaß, Leonard Rossmann, and Marcel Nicolaus
The Cryosphere, 18, 2001–2015, https://doi.org/10.5194/tc-18-2001-2024, https://doi.org/10.5194/tc-18-2001-2024, 2024
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Antarctic sea ice maintains year-round snow cover, crucial for its energy and mass budgets. Despite its significance, snow depth remains poorly understood. Over the last decades, Snow Buoys have been deployed extensively on the sea ice to measure snow accumulation but not actual depth due to snow transformation into meteoric ice. Therefore, in this study we utilize sea ice and snow models to estimate meteoric ice fractions in order to calculate actual snow depth in the Weddell Sea.
Dunwang Lu, Jianqiang Liu, Lijian Shi, Tao Zeng, Bin Cheng, Suhui Wu, and Manman Wang
The Cryosphere, 18, 1419–1441, https://doi.org/10.5194/tc-18-1419-2024, https://doi.org/10.5194/tc-18-1419-2024, 2024
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We retrieved sea ice drift in Fram Strait using the Chinese HaiYang 1D Coastal Zone Imager. The dataset is has hourly and daily intervals for analysis, and validation is performed using a synthetic aperture radar (SAR)-based product and International Arctic Buoy Programme (IABP) buoys. The differences between them are explained by investigating the spatiotemporal variability in sea ice motion. The accuracy of flow direction retrieval for sea ice drift is also related to sea ice displacement.
Moein Mellat, Amy R. Macfarlane, Camilla F. Brunello, Martin Werner, Martin Schneebeli, Ruzica Dadic, Stefanie Arndt, Kaisa-Riikka Mustonen, Jeffrey M. Welker, and Hanno Meyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-719, https://doi.org/10.5194/egusphere-2024-719, 2024
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Our research, utilizing data from the Arctic MOSAiC expedition, reveals how snow on Arctic sea ice changes due to weather conditions. By analyzing snow samples collected over a year, we found differences in snow layers that tell us about their origins and how they've been affected by the environment. We discovered variations in snow and vapour that reflect the influence of weather patterns and surface processes like wind and sublimation.
Evelyn Jäkel, Sebastian Becker, Tim R. Sperzel, Hannah Niehaus, Gunnar Spreen, Ran Tao, Marcel Nicolaus, Wolfgang Dorn, Annette Rinke, Jörg Brauchle, and Manfred Wendisch
The Cryosphere, 18, 1185–1205, https://doi.org/10.5194/tc-18-1185-2024, https://doi.org/10.5194/tc-18-1185-2024, 2024
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The results of the surface albedo scheme of a coupled regional climate model were evaluated against airborne and ground-based measurements conducted in the European Arctic in different seasons between 2017 and 2022. We found a seasonally dependent bias between measured and modeled surface albedo for cloudless and cloudy situations. The strongest effects of the albedo model bias on the net irradiance were most apparent in the presence of optically thin clouds.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
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Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 18, 957–976, https://doi.org/10.5194/tc-18-957-2024, https://doi.org/10.5194/tc-18-957-2024, 2024
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A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the sensible and latent heat fluxes in the Arctic. We analyse these effects utilising four data sets called atmospheric reanalyses, and we evaluate uncertainties in these effects arising from inter-reanalysis differences in SIC and in the sensitivity of the latent and sensible heat fluxes to SIC.
Miao Yu, Peng Lu, Matti Leppäranta, Bin Cheng, Ruibo Lei, Bingrui Li, Qingkai Wang, and Zhijun Li
The Cryosphere, 18, 273–288, https://doi.org/10.5194/tc-18-273-2024, https://doi.org/10.5194/tc-18-273-2024, 2024
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Variations in Arctic sea ice are related not only to its macroscale properties but also to its microstructure. Arctic ice cores in the summers of 2008 to 2016 were used to analyze variations in the ice inherent optical properties related to changes in the ice microstructure. The results reveal changing ice microstructure greatly increased the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness was assumed, especially in marginal ice zones.
Amy R. Macfarlane, Henning Löwe, Lucille Gimenes, David N. Wagner, Ruzica Dadic, Rafael Ottersberg, Stefan Hämmerle, and Martin Schneebeli
The Cryosphere, 17, 5417–5434, https://doi.org/10.5194/tc-17-5417-2023, https://doi.org/10.5194/tc-17-5417-2023, 2023
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Snow acts as an insulating blanket on Arctic sea ice, keeping the underlying ice "warm", relative to the atmosphere. Knowing the snow's thermal conductivity is essential for understanding winter ice growth. During the MOSAiC expedition, we measured the thermal conductivity of snow. We found spatial and vertical variability to overpower any temporal variability or dependency on underlying ice type and the thermal resistance to be directly influenced by snow height.
Lejiang Yu, Shiyuan Zhong, Timo Vihma, Cuijuan Sui, and Bo Sun
EGUsphere, https://doi.org/10.5194/egusphere-2023-2436, https://doi.org/10.5194/egusphere-2023-2436, 2023
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In contrary to the current understanding, there can be a strong connection between ENSO and the South Atlantic Subtropical Dipole (SASD). It is highly probable that the robust inverse correlation between ENSO and SASD will persist in the future. The ENSO-SASD correlation exhibits substantial multi-decadal variability over the course of a century. The change in the ENSO-SASD relation can be linked to changes in ENSO regime and convective activities over the central South Pacific Ocean.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
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The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Tiina Nygård, Lukas Papritz, Tuomas Naakka, and Timo Vihma
Weather Clim. Dynam., 4, 943–961, https://doi.org/10.5194/wcd-4-943-2023, https://doi.org/10.5194/wcd-4-943-2023, 2023
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Despite the general warming trend, wintertime cold-air outbreaks in Europe have remained nearly as extreme and as common as decades ago. In this study, we identify six principal cold anomaly types over Europe in 1979–2020. We show the origins of various physical processes and their contributions to the formation of cold wintertime air masses.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Melanie Lauer, Annette Rinke, Irina Gorodetskaya, Michael Sprenger, Mario Mech, and Susanne Crewell
Atmos. Chem. Phys., 23, 8705–8726, https://doi.org/10.5194/acp-23-8705-2023, https://doi.org/10.5194/acp-23-8705-2023, 2023
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We present a new method to analyse the influence of atmospheric rivers (ARs), cyclones, and fronts on the precipitation in the Arctic, based on two campaigns: ACLOUD (early summer 2017) and AFLUX (early spring 2019). There are differences between both campaign periods: in early summer, the precipitation is mostly related to ARs and fronts, especially when they are co-located, while in early spring, cyclones isolated from ARs and fronts contributed most to the precipitation.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
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Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
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We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Julia Martin and Martin Schneebeli
The Cryosphere, 17, 1723–1734, https://doi.org/10.5194/tc-17-1723-2023, https://doi.org/10.5194/tc-17-1723-2023, 2023
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The grain size of snow determines how light is reflected and other physical properties. The IceCube measures snow grain size at the specific near-infrared wavelength of 1320 nm. In our study, the preparation of snow samples for the IceCube creates a thin layer of small particles. Comparisons of the grain size with computed tomography, particle counting and numerical simulation confirm the aforementioned observation. We conclude that measurements at this wavelength underestimate the grain size.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Na Li, Ruibo Lei, Petra Heil, Bin Cheng, Minghu Ding, Zhongxiang Tian, and Bingrui Li
The Cryosphere, 17, 917–937, https://doi.org/10.5194/tc-17-917-2023, https://doi.org/10.5194/tc-17-917-2023, 2023
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The observed annual maximum landfast ice (LFI) thickness off Zhongshan (Davis) was 1.59±0.17 m (1.64±0.08 m). Larger interannual and local spatial variabilities for the seasonality of LFI were identified at Zhongshan, with the dominant influencing factors of air temperature anomaly, snow atop, local topography and wind regime, and oceanic heat flux. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Marcel Nicolaus, Fanyi Zhang, Benjamin Rabe, Long Lin, Julia Regnery, and Donald K. Perovich
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-25, https://doi.org/10.5194/tc-2023-25, 2023
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To characterize the freezing and melting of different types of sea ice, we deployed four IMBs during the MOSAiC second drift. The drifting pattern, together with a large snow accumulation, relatively warm air temperatures, and a rapid increase in oceanic heat close to Fram Strait, determined the seasonal evolution of the ice mass balance. The refreezing of ponded ice and voids within the unconsolidated ridges amplifies the anisotropy of the heat exchange between the ice and the atmosphere/ocean.
Lejiang Yu, Shiyuan Zhong, Timo Vihma, Cuijuan Sui, and Bo Sun
Atmos. Chem. Phys., 23, 345–353, https://doi.org/10.5194/acp-23-345-2023, https://doi.org/10.5194/acp-23-345-2023, 2023
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Previous studies have noted a significant relationship between the Subtropical Indian Ocean Dipole and the South Atlantic Ocean Dipole indices, but little is known about the stability of their relationship. We found a significant positive correlation between the two indices prior to the year 2000 but an insignificant correlation afterwards.
Julian Gutt, Stefanie Arndt, David Keith Alan Barnes, Horst Bornemann, Thomas Brey, Olaf Eisen, Hauke Flores, Huw Griffiths, Christian Haas, Stefan Hain, Tore Hattermann, Christoph Held, Mario Hoppema, Enrique Isla, Markus Janout, Céline Le Bohec, Heike Link, Felix Christopher Mark, Sebastien Moreau, Scarlett Trimborn, Ilse van Opzeeland, Hans-Otto Pörtner, Fokje Schaafsma, Katharina Teschke, Sandra Tippenhauer, Anton Van de Putte, Mia Wege, Daniel Zitterbart, and Dieter Piepenburg
Biogeosciences, 19, 5313–5342, https://doi.org/10.5194/bg-19-5313-2022, https://doi.org/10.5194/bg-19-5313-2022, 2022
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Long-term ecological observations are key to assess, understand and predict impacts of environmental change on biotas. We present a multidisciplinary framework for such largely lacking investigations in the East Antarctic Southern Ocean, combined with case studies, experimental and modelling work. As climate change is still minor here but is projected to start soon, the timely implementation of this framework provides the unique opportunity to document its ecological impacts from the very onset.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
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Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Elena Shevnina, Miguel Potes, Timo Vihma, Tuomas Naakka, Pankaj Ramji Dhote, and Praveen Kumar Thakur
The Cryosphere, 16, 3101–3121, https://doi.org/10.5194/tc-16-3101-2022, https://doi.org/10.5194/tc-16-3101-2022, 2022
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The evaporation over an ice-free glacial lake was measured in January 2018, and the uncertainties inherent to five indirect methods were quantified. Results show that in summer up to 5 mm of water evaporated daily from the surface of the lake located in Antarctica. The indirect methods underestimated the evaporation over the lake's surface by up to 72 %. The results are important for estimating the evaporation over polar regions where a growing number of glacial lakes have recently been evident.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Annakaisa von Lerber, Mario Mech, Annette Rinke, Damao Zhang, Melanie Lauer, Ana Radovan, Irina Gorodetskaya, and Susanne Crewell
Atmos. Chem. Phys., 22, 7287–7317, https://doi.org/10.5194/acp-22-7287-2022, https://doi.org/10.5194/acp-22-7287-2022, 2022
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Snowfall is an important climate indicator. However, microphysical snowfall processes are challenging for atmospheric models. In this study, the performance of a regional climate model is evaluated in modeling the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations. Excellent agreement in averaged annual snowfall rates is found, and the shown methodology offers a promising diagnostic tool to investigate the shown differences further.
Janosch Michaelis, Amelie U. Schmitt, Christof Lüpkes, Jörg Hartmann, Gerit Birnbaum, and Timo Vihma
Earth Syst. Sci. Data, 14, 1621–1637, https://doi.org/10.5194/essd-14-1621-2022, https://doi.org/10.5194/essd-14-1621-2022, 2022
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A major goal of the Springtime Atmospheric Boundary Layer Experiment (STABLE) aircraft campaign was to observe atmospheric conditions during marine cold-air outbreaks (MCAOs) originating from the sea-ice-covered Arctic ocean. Quality-controlled measurements of several meteorological variables collected during 15 vertical aircraft profiles and by 22 dropsondes are presented. The comprehensive data set may be used for validating model results to improve the understanding of future trends in MCAOs.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
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We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
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A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
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Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Carolina Viceto, Irina V. Gorodetskaya, Annette Rinke, Marion Maturilli, Alfredo Rocha, and Susanne Crewell
Atmos. Chem. Phys., 22, 441–463, https://doi.org/10.5194/acp-22-441-2022, https://doi.org/10.5194/acp-22-441-2022, 2022
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We focus on anomalous moisture transport events known as atmospheric rivers (ARs). During ACLOUD and PASCAL, three AR events were identified: 30 May, 6 June, and 9 June 2017. We explore their spatio-temporal evolution and precipitation patterns using measurements, reanalyses, and a model. We show the importance of the following: Atlantic and Siberian pathways during spring–summer in the Arctic, AR-associated heat/moisture increase, precipitation phase transition, and high-resolution datasets.
Hélène Bresson, Annette Rinke, Mario Mech, Daniel Reinert, Vera Schemann, Kerstin Ebell, Marion Maturilli, Carolina Viceto, Irina Gorodetskaya, and Susanne Crewell
Atmos. Chem. Phys., 22, 173–196, https://doi.org/10.5194/acp-22-173-2022, https://doi.org/10.5194/acp-22-173-2022, 2022
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Arctic warming is pronounced, and one factor in this is the poleward atmospheric transport of heat and moisture. This study assesses the 4D structure of an Arctic moisture intrusion event which occurred in June 2017. For the first time, high-resolution pan-Arctic ICON simulations are performed and compared with global models, reanalysis, and observations. Results show the added value of high resolution in the event representation and the impact of the intrusion on the surface energy fluxes.
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Short summary
We study snow density from the MOSAiC expedition. Several snow density schemes were tested and compared with observation. A thermodynamic ice model was employed to assess the impact of snow density and precipitation on the thermal regime of sea ice. The parameterized mean snow densities are consistent with observations. Increased snow density reduces snow and ice temperatures, promoting ice growth, while increased precipitation leads to warmer snow and ice temperatures and reduced ice thickness.
We study snow density from the MOSAiC expedition. Several snow density schemes were tested and...
